Safe and Robust Machine Learning for Real Systems

Dr. Farinaz Koushanfar

University of California San Diego

Friday, October 15, 2021

11 AM – 12 PM CST

https://utsa.zoom.us/j/98953233499

We are at the CUSP of the fourth industrial revolution empowered by machine learning and application automation: seamlessly connecting people, data, and computing machines. Such intelligent technologies however bring numerous potential security vulnerabilities and threats that might severely compromise their safety. In this talk, I present our work in providing end-to-end solutions for practicable robust machine learning based upon co-design and optimization of ML, data, hardware and software. The goal is to characterize the potential ML attack surface, identify and address the nefarious threats in real-time, and devise novel solutions to tackle these problems. In particular, I will show how our work marks the first set of metrics, as well as real-time resource-efficient solutions for machine learning vulnerability characterization, adversarial attacks, and data poisoning.  I conclude by briefly discussing the challenges and opportunities moving forward.